02345nas a2200325   4500008004100000022001400041245014200055210006900197260001600266300001600282490000800298520136100306100001901667700001601686700002001702700001901722700002501741700002101766700002001787700001801807700001701825700001901842700002201861700002101883700001501904700001601919700001501935700001801950856005101968       2018                        eng d  a0001-496600aDeep learning for ethoacoustical mapping: Application to a single Cachalot long term recording on joint observatories in Vancouver Island0 aDeep learning for ethoacoustical mapping Application to a single  cJan-09-2018  a1776 - 17770 v1443 a<p>During February and March, 2018, a lone sperm whale known as Yukusam was recorded first by Orcalab in Johnstone Strait and subsequently on multiple hydrophones within the Salish Sea [1]. We learn and denoise these multichannel clicks trains with AutoEncoders Convolutional Neural Net (CNN). Then, we build a map of the echolocations to elucidate variations in the acoustic behavior of this unique animal over time, in different environments and distinct levels of boat noise. If CNN approximates an optimal kernel decomposition, it requires large amounts of data. Via spline functionals we offer analytics kernels with learnable coefficients do reduce it. We [1-3] identify the analytical mother wavelet to represent the input signal to directly learn the wavelet support from scratch by gradient descend on the parameters of cubic splines [2]. Supplemental material http://sabiod.org/yukusam [1] Balestriero, Roger, Glotin, Baraniuk, Semi-Supervised Learning via New Deep Network Inversion, arXiv preprint arXiv:1711.04313, 2017 [2] Balestriero, Cosentino, Glotin, Baraniuk, WaveletNet : Spline Filters for End-to-End Deep Learning, Int. Conf. on MachineLearning, ICML, Stockholm, http://sabiod.org/bib, 2018 [3] Spong P., Symonds H., et al., Joint Observatories Following a Single male Cachalot during 12 weeks&mdash;The Yukusam story, ASA 2018.</p>
1 aGlotin, Hervé1 aSpong, Paul1 aSymonds, Helena1 aRoger, Vincent1 aBalestriero, Randall1 aFerrari, Maxence1 aPoupard, Marion1 aTowers, Jared1 aVeirs, Scott1 aMarxer, Ricard1 aGiraudet, Pascale1 aPilkinton, James1 aVeirs, Val1 aWood, Jason1 aFord, John1 aDakin, Thomas  uhttp://asa.scitation.org/doi/10.1121/1.506785501900nas a2200193   4500008004100000022001400041245008000055210006900135260001600204300001600220490000800236520131000244100001901554700002101573700001901594700002301613700001901636856005101655       2018                        eng d  a0001-496600aTowards the topology of autoencoder of calls versus clicks of marine mammal0 aTowards the topology of autoencoder of calls versus clicks of ma  cJan-09-2018  a1777 - 17780 v1443 a<p>The goal is to learn the features and the representation adapted for cetacean sound dynamics without any priors. Thus, we develop data driven model to generate voicing and click of cetaceans audio signals. We learn representation and features of stationary or nonstationary emission using neural network from raw audio. We use different types of convolutions (causal, with strides, with dilation [1]), or gradient inversion [2]. Experiments are conducted on various kind of calls of humpback whales from nips4b challenge [3] or Orca whale. We compare the topology for transient encoding on Physeters and Inia g. For each model, we detail the resulting filters and discuss on the topology. We acknowledge Region PACA and NortekMED for Roger&rsquo;s Phd grant, &amp; DGA and R&eacute;gion Haut de France for Ferrari&rsquo;s Phd grant. [1] Oord, Dieleman, Zen, Simonyan, Vinyals, Graves et al. Wavenet : A generative model for raw audio, arXiv:1609.03499, 2016 [2] Balestriero, Roger, Glotin, Baraniuk, Semi-Supervised Learning via New Deep Network Inversion, arXiv:1711.04313, 2017 [3] Glotin, LeCun, Mallat et al. Proc. 1st wkp on Neural Information Processing for Bioacoustics NIPS4B, joint to NIPS Alberta USA, 2013 http://sabiod.org/nips4b/challenge2.html, http://sabiod.org/NIPS4B2013_book.pdf</p>
1 aRoger, Vincent1 aFerrari, Maxence1 aMarxer, Ricard1 aChamroukhi, Faicel1 aGlotin, Hervé  uhttp://asa.scitation.org/doi/10.1121/1.5067859